AUTHOR=Church Kenneth , Liu Boxiang 

TITLE=Acronyms and Opportunities for Improving Deep Nets

JOURNAL=Frontiers in Artificial Intelligence

VOLUME=Volume 4 - 2021

YEAR=2021

URL=https://www.frontiersin.org/journals/artificial-intelligence/articles/10.3389/frai.2021.732381

DOI=10.3389/frai.2021.732381

ISSN=2624-8212

ABSTRACT=Recently, several studies have reported promising results with BERT-like methods on acronym tasks. In this study, we find an older rule-based program, Ab3P, not only performs better, but error analysis suggests why.
There is a
well-known spelling convention in acronyms
where each letter in the short form (SF) refers to ``salient'' letters in the long form (LF).
The error analysis uses decision trees and logistic regression to show that there is an opportunity for many pre-trained models (BERT, T5, BioBert, BART, ERNIE) to take advantage of this spelling convention.